Anomaly Detection with Conditioned Denoising Diffusion Models.

Official implementation of DDAD

Requirements

This repository is implemented and tested on Python 3.8 and PyTorch 2.1. To install requirements:

pip install -r requirements.txt

Train and Evaluation of the Model

You can download the model checkpoints directly from Checkpoints

To train the denoising UNet, run:

python main.py --train True

Modify the settings in the config.yaml file to train the model on different categories.

For fine-tuning the feature extractor, use the following command:

python main.py --domain_adaptation True

To evaluate and test the model, run:

python main.py --detection True

Dataset

You can download MVTec AD: MVTec Software and VisA Benchmarks. For preprocessing of VisA dataset check out the Data preparation section of this repository.

The dataset should be placed in the 'datasets' folder. The training dataset should only contain one subcategory consisting of nominal samples, which should be named 'good'. The test dataset should include one category named 'good' for nominal samples, and any other subcategories of anomalous samples. It should be made as follows:

Name_of_Dataset
|-- Category
|-----|----- ground_truth
|-----|----- test
|-----|--------|------ good
|-----|--------|------ ...
|-----|--------|------ ...
|-----|----- train
|-----|--------|------ good

Results

Running the code as explained in this file should achieve the following results for MVTec AD:

Anomaly Detection (Image AUROC) and Anomaly Localization (Pixel AUROC, PRO)

Expected results for MVTec AD:

Category	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazel nut	Metalnut	Pill	Screw	Toothbrush	Transistor	Zipper	Average
Detection	99.3%	100%	100%	100%	100%	100%	99.4%	99.4%	100%	100%	100%	99.0%	100%	100%	100%	99.8%
Localization	(98.7%,93.9%)	(99.4%,97.3%)	(99.4%,97.7%)	(98.2%,93.1%)	(95.0%,82.9%)	(98.7%,91.8%)	(98.1%,88.9%)	(95.7%,93.4%)	(98.4%,86.7%)	(99.0%,91.1%)	(99.1%,95.5%)	(99.3%,96.3%)	(98.7%,92.6%)	(95.3%,90.1%)	(98.2%,93.2%)	(98,1%,92.3%)

The settings used for these results are detailed in the table.

Categories	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal nut	Pill	Screw	Toothbrush	Transistor	Zipper
(w)	0	4	11	4	11	3	3	8	5	7	9	2	0	0	10
Training epochs	2500	2000	2000	1000	2000	1000	3000	1500	2000	3000	1000	2000	2000	2000	1000
FE epochs	0	6	8	0	16	5	0	8	3	1	4	4	2	0	6

Following is the expected results on VisA Dataset.

Category	Candle	Capsules	Cashew	Chewing gum	Fryum	Macaroni1	Macaroni2	PCB1	PCB2	PCB3	PCB4	Pipe fryum	Average
Detection	99.9%	100%	94.5%	98.1%	99.0%	99.2%	99.2%	100%	99.7%	97.2%	100%	100%	98.9%
Localization	(98.7%,96.6%)	(99.5%,95.0%)	(97.4%,80.3%)	(96.5%,85.2%)	(96.9%,94.2%)	(98.7%,98.5%)	(98.2%,99.3%)	(93.4%,93.3%)	(97.4%,93.3%)	(96.3%,86.6%)	(98.5%,95.5%)	(99.5%,94.7%)	(97.6%,92.7%)

The settings used for these results are detailed in the table.

Categories	Candle	Capsules	Cashew	Chewing gum	Fryum	Macaroni1	Macaroni2	PCB1	PCB2	PCB3	PCB4	Pipe fryum
(w)	6	5	0	6	4	5	2	9	5	6	6	8
Training epochs	1000	1000	1750	1250	1000	500	500	500	500	500	500	500
FE epochs	1	3	0	0	3	7	11	8	5	1	1	6

Citation

@article{mousakhan2023anomaly,
  title={Anomaly Detection with Conditioned Denoising Diffusion Models},
  author={Mousakhan, Arian and Brox, Thomas and Tayyub, Jawad},
  journal={arXiv preprint arXiv:2305.15956},
  year={2023}
}

Feedback

For any feedback or inquiries, please contact arian.mousakhan@gmail.com